Artificial intelligence (AI)-based methods continue to make inroads into accelerated
materials design and development. Here, we review AI-enabled advances made in the …

Abstract Machine learning (ML) has been rapidly transforming the landscape of natural
sciences and has the potential to revolutionize the process of data analysis and hypothesis …

Electrostatic capacitors play a crucial role as energy storage devices in modern electrical
systems. Energy density, the figure of merit for electrostatic capacitors, is primarily …

The complexity and diversity of polymer topologies, or chain architectures, present
substantial challenges in predicting and engineering polymer properties. Although machine …

The forward screening and reverse design of drug molecules, inorganic molecules, and
polymers with enhanced properties are vital for accelerating the transition from laboratory …

The BigSMILES notation, a concise tool for polymer ensemble representation, is augmented
here by introducing an enhanced version called generative BigSMILES. G-BigSMILES is …

Materials informatics (MI) has immense potential to accelerate the pace of innovation and
new product development in biotechnology. Close collaborations between skilled physical …

Artificial intelligence (AI) promotes the polymer design paradigm from a traditional trial-and-
error approach to a data-driven style. Achieving high thermal conductivity (TC) for intrinsic …

Defining the similarity between chemical entities is an essential task in polymer informatics,
enabling ranking, clustering, and classification. Despite its importance, the pairwise …

Recent advances in machine learning have led to the rapid adoption of various
computational methods for de novo molecular design in polymer research, including high …